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December 1, 20131,189 citations

Efficient Image Dehazing with Boundary Constraint and Contextual Regularization

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GMGaofeng MengYWYing WangJDJiangyong Duan

Key Points

  • This research aims to develop an effective method for removing haze from images captured in foggy conditions.
  • Proposed a regularization method utilizing boundary constraints on the transmission function.
  • Implemented a weighted L_1-norm based contextual regularization in an optimization framework.
  • Developed a variable splitting algorithm to efficiently solve the optimization problem.
  • Achieved high-quality haze-free images with accurate colors and fine details.
  • Demonstrated effectiveness on various haze images, showing significant improvement in image visibility.

Abstract

Images captured in foggy weather conditions often suffer from bad visibility. In this paper, we propose an efficient regularization method to remove hazes from a single input image. Our method benefits much from an exploration on the inherent boundary constraint on the transmission function. This constraint, combined with a weighted L₁-norm based contextual regularization, is modeled into an optimization problem to estimate the unknown scene transmission. A quite efficient algorithm based on variable splitting is also presented to solve the problem. The proposed method requires only a few general assumptions and can restore a high-quality haze-free image with faithful colors and fine image details. Experimental results on a variety of haze images demonstrate the effectiveness and efficiency of the proposed method.

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Cite This Study

Meng et al. (2013) studied this question.

synapsesocial.com/papers/6a04d1e219daca77e62d3436https://doi.org/10.1109/iccv.2013.82
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